Rolling Conformal Prediction in Sequential Model Training
Rolling Conformal Prediction is introduced, a distribution-free predictive inference method for the setting of sequential model training that proves high-probability training-conditional validity uniformly over time; under stability conditions, coverage guarantees sharpen towards $1-\alpha$.
Chen Cheng, Rui-Ting Liang, Rina Foygel Barber
· 0 citations